Meta ad builder

Turn a finished creative — typically the output of a generative-AI skill in this workspace — into a live Meta ad. The skill covers three phases: research (optional) → copy → deploy. It talks to the Meta Marketing API directly via ported, parameterized Python scripts.

When to use this skill

Trigger on phrases like:

• "deploy this video to Meta" / "publish this as a Facebook ad"
• "build/launch a Meta ad" / "push this creative into <ad set>"
• "create a Meta ad with this image and copy"
• "pull my top-performing ads" / "what are my best ads by ROAS"
• "research competitor ads" / "pull <brand>'s ads from the Ad Library"

Do not use this skill to generate creative — that's pixar-style-ad, claymation-ad, generate-youtube-thumbnail, uni1-image-ad, etc. Do not use it to write AdTable/Airtable rows — that's adtable-light. This skill is the direct Meta Marketing API deployment step.

Read order

1. This file — workflow, decision tree, safety rules.
2. prompting/copy-guide.md — the 5-body / 5-title / 3-description frameworks and the --copy-file JSON shape.
3. reference/deploy-patterns.md — creative spec mechanics, video polling, retry, failure modes.
4. reference/meta-api-cheatsheet.md — the full Meta Marketing API reference (campaigns, ad sets, ads, enums, gotchas, Ad Library). Consult as needed; don't read end-to-end.

Prerequisites

• Env (in .env — see your repo's .env.example):
  • META_ACCESS_TOKEN (required) — long-lived token with ads_management scope
  • META_AD_ACCOUNT_ID (required) — with or without the act_ prefix
  • META_PAGE_ID, META_IG_USER_ID, META_PIXEL_ID (optional defaults for deploy)
  • META_API_VERSION (optional, default v23.0)
• Python deps: python3 -m pip install -r scripts/requirements.txt
• A target ad set that already exists. The skill deploys ads into an existing ad set — it does not create campaigns or ad sets. If the user needs a new ad set, create it in Ads Manager or via the cheatsheet §3–§4 first.
• The finished creative on disk — an image or video file path. Chat-pasted files are not accessible; ask the user for a real path.

Run bash scripts/check-meta-env.sh to verify credentials before anything else.

Workflow

Phase 1 — Research (optional, when copy should model winners)

# Rank the account's ads and pull the winning copy
python scripts/pull-top-ads.py --date-preset last_30d --min-spend 100 --limit 15

# Pull a competitor's ads from the Ad Library
python scripts/pull-competitor-ads.py --pages "BrandName" --limit 50

Both write JSON under OUTPUT_BASE (or ./outputs/meta-ads/). Read the top-ad copy fields to identify winning hook/proof/CTA patterns before writing new copy.

Phase 2 — Copy

Write a copy.json following copy-guide.md: 5 bodies (one per framework angle), 5 titles, 3 descriptions. If Phase 1 ran, mirror the voice and patterns of the winners — new creative + proven copy DNA. Save copy.json somewhere under outputs/ so it isn't committed.

Phase 3 — Deploy

Always dry-run first — it prints the full creative payload, makes no API calls:

python scripts/deploy-ad.py --dry-run \
  --adset-id <AD_SET_ID> --copy-file copy.json --link <DESTINATION_URL> \
  --image path/to/creative.png

Review the payload with the user, then deploy for real:

python scripts/deploy-ad.py \
  --adset-id <AD_SET_ID> --copy-file copy.json --link <DESTINATION_URL> \
  --video clip-a.mp4 --video clip-b.mp4 --cta SIGN_UP --pixel-id <PIXEL_ID>

• --image / --video are repeatable — each becomes its own ad in the ad set.
• Every ad is created PAUSED. Tell the user to review and un-pause in Meta Ads Manager. The skill never launches a spending ad automatically.
• Results (ad IDs) are written to deployment_results.json under OUTPUT_BASE.

Decision tree

User intent	Phases
"Deploy this creative to Meta" + copy provided	Phase 3 only
"Build a Meta ad, write the copy too"	Phase 2 → 3
"Make ads modeled on my winners"	Phase 1 → 2 → 3
"What are my best ads / competitor research"	Phase 1 only

Safety rules

• Ads deploy PAUSED. Never add an --active override or un-pause ads without an explicit user instruction. Confirm the user knows the ads are paused.
• Dry-run before every real deploy. Show the payload; get a go-ahead.
• Confirm the target ad set and destination URL with the user before deploying — an ad in the wrong ad set spends against the wrong budget.
• Deploying ads is a shared-state, money-adjacent action. Treat the live deploy-ad.py run as something to confirm, not assume.
• The skill creates ads only — it does not create or edit campaigns, ad sets, budgets, or audiences. Those stay manual.

Quirks and pitfalls

• Video processing is async. deploy-ad.py polls the uploaded video until Meta finishes processing before creating the ad — a video deploy can take a few minutes. See deploy-patterns.md.
• Transient OAuthException (code 2). Retried automatically with backoff.
• act_ prefix is added automatically if missing from META_AD_ACCOUNT_ID.
• Account-specific data stays out of git. All output routes through outputs/ (gitignored): ad IDs, pulled spend/revenue, competitor data.
• Special Ad Categories (credit, employment, housing, social issues) change ad-set targeting rules — flag to the user; see cheatsheet §3.5.

Cost note

The Meta Marketing API itself is free to call. Ad spend is real money — but because every ad deploys PAUSED, nothing spends until the user un-pauses it in Ads Manager. There are no per-call credits to estimate.